1. Field of the Invention
The present invention relates to an apparatus and method of classifying and extracting features of a fingerprint or palm print and, particularly, to a fingerprint feature detecting apparatus and method for detecting features of a fingerprint.
2. Description of the Related Art
Examples of a prior art method of detecting features of a fingerprint using a computer is disclosed in a paper entitled "Automated Fingerprint Classifier" by F. Noda, S. Ohteru, H. Kobayashi and T. Kato, published in the Proceedings of 2nd International Pattern Recognition Conference, August, 1974 and in a paper entitled "Fingerprint Classification by Directional Distribution Patterns" by O. Nakamura, K. Goto and T. Minami, published in the Transaction of the Institute of Electronics, Information and Communication Engineers, Vol. J65-D, No. 10, October 1982, pp. 1286-1293.
In the former example which will be referred to as the Noda et al method, a fingerprint is considered as a vector field and a feature thereof is detected by performing a surface integral of divergence in a local area thereof and, in the latter example which will be referred to as the Nakamura et al method, a fingerprint feature is detected by patterning a directional distribution of ridges and matching a resulting pattern with a preliminarily prepared standard pattern.
The Nakamura et al method, which is closer to the present invention than Noda et al method, will be described with reference to FIG. 17.
In the Nakamura et al method, a directional distribution pattern generating unit 302 produces a directional distribution pattern of ridges in an area around a certain mark point on a fingerprint such as shown in FIG. 18, a distance calculation unit 303 calculates a distance between the directional distribution pattern thus produced and a preliminarily produced standard pattern, and a distance determination unit 304 determines a category corresponding to the standard pattern having the minimum distance to the produced pattern.
First, a processing in the directional distribution pattern producing unit 302 will be described. As shown in FIG. 19, a circular area of the fingerprint having the mark point as a center is divided to 16 sectors and, among angles of ridge directions j with respect to a center line L.sub.i in a sector area W.sub.i, small angles (not larger than 90.degree.) are represented by r.sup.j.sub.i. An average C.sub.i of r.sup.j.sub.i for N ridge directions contained in the sector area W.sub.i is obtained according to the following equation (1): ##EQU1## and a two dimensional directional distribution pattern of all of the 16 sectors areas is obtained by repeating this processing for all of the sectors.
The directional pattern of ridges is classified to four categories, first to third categories including feature patterns, "whorl", "loop" and "delta", respectively, and a fourth category including non-feature patterns called "periphery". The respective whorl and loop are referred to as "core". The directional distribution patterns of whorl, loop, delta and periphery are two dimensional patterns specific to the respective categories as shown in FIG. 20.
Next, a processing performed in the distance calculation unit 303 will be described. In order to classify these two dimensional patterns, it is necessary to preliminarily select feature points and peripheral portions of a directional pattern of ridges visually and prepare standard patterns C.sup.t for the respective categories. The distance calculation unit 303 calculates a distance D between the standard pattern and the directional pattern of ridges according to the following equation (2): ##EQU2## where the letter S denotes an amount of rotation of the standard pattern in calculating the distance. That is, the minimum distance is D among a plurality of distances obtained by matching while rotating the standard pattern for every constant angle shift.
The distance determining unit 304 compares the minimum distances for these categories with each other to determine a fingerprint feature whorl, loop or delta to which the standard pattern having the minimum distance belongs, and outputs the feature thus obtained as a result 305 of the detection.
In the conventional fingerpring feature detecting method, however, it is necessary to preliminarily select a feature point visually and prepare the standard pattern by using the directional distribution pattern in the vicinity of the feature point, which is a time consuming procedure. Further, since the distance calculation is performed between quantized patterns, the distance value depends on rotation of the directional pattern of ridges, and thus the detection accuracy is degraded.
This fact will be described by employing a case of delta as a feature with reference to FIGS. 21 and 22. A directional distribution of the delta pattern includes three protrusions and three valleys each between adjacent ones of the protrusions as shown by the thick solid line in FIG. 21. The directional distribution pattern is put on the 16 sector areas and the directional distribution pattern is sampled at points indicated by the small black squares. On the other hand, when the same directional distribution pattern is rotated counterclockwise by (360/32) degrees, a directional distribution pattern shown in FIG. 22 is obtained. Then, when the directional distribution pattern thus obtained is put on an area divided to 16 sectors, the pattern is sampled at points indicated by the small black squares. Although the directional distribution patterns shown in FIGS. 21 and 22 are the same originally, the distance does not become zero even if the matching is performed while rotating in a stepwise manner one with respect to the other by a constant angle, due to the difference in sampling point between the patterns. Therefore, the distance value depends on rotation of the fingerprint and thus the detection accuracy is degraded.
An object of the present invention is to provide a fingerprint feature detecting apparatus for detecting fingerprint feature, which does not require a preliminary preparation of standard pattern, does not depend on the rotation of a directional pattern of ridges, and is capable of maintaining high detection accuracy.
Another object of the present invention is to provide a fingerprint feature detecting method for detecting a fingerprint feature, which does not require a preliminary preparation of standard pattern, does not depend on the rotation of a directional pattern of ridges, and is capable of maintaining high detection accuracy.